A hybrid neural model in long-term electrical load forecasting

Authors:
Otávio A. S. Carpinteiro;Isaías Lima;Rafael C. Leme;Antonio C. Zambroni de Souza;Edmilson M. Moreira;Carlos A. M. Pinheiro
Affiliations:
Research Group on Computer Networks and Software Engineering, Federal University of Itajubá, Itajubá, MG, Brazil;Research Group on Computer Networks and Software Engineering, Federal University of Itajubá, Itajubá, MG, Brazil;Research Group on Computer Networks and Software Engineering, Federal University of Itajubá, Itajubá, MG, Brazil;Research Group on Computer Networks and Software Engineering, Federal University of Itajubá, Itajubá, MG, Brazil;Research Group on Computer Networks and Software Engineering, Federal University of Itajubá, Itajubá, MG, Brazil;Research Group on Computer Networks and Software Engineering, Federal University of Itajubá, Itajubá, MG, Brazil
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

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Abstract

A novel hierarchical hybrid neural model to the problem of long-term electrical load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets — one on top of the other —, and a single-layer perceptron. It has application into domains which require time series analysis. The model is compared to a multilayer perceptron. Both the hierarchical and the multilayer perceptron models are endowed with time windows in their input layers. They are trained and assessed on load data extracted from a North-American electric utility. The models are required to predict once every week the electric peak-load and mean-load during the next two years. The results are presented and evaluated in the paper.